Parameter Identification of Fractional Order Chaotic System via Opposition Based Learning Bare-Bones Imperialist Competition Algorithm

Abstract

In this paper, a new method is proposed to identify the parameters of fractional order chaotic system. The parameter identification is achieved by minimizing the mean square error between the states of original fractional chaotic system and those of the estimated one, in which the parameters to be identified are regarded as the optimization variables. To effectively solve the optimization problem, an improved meta-heuristic algorithm, i.e., opposition based learning (OBL) Bare-bones imperialist competition algorithm (OBL-BBICA), is proposed. The proposed OBL-BBICA introduces the OBL and Gaussian sampling into imperialist competition algorithm (ICA) to enhance the exploration ability of ICA, and thus, overcomes the drawbacks of premature phenomena of ICA. OBL-BBICA is adopted to search the optimal parameters of fractional order chaotic system. Experimental results show that the proposed method can accurately identify the parameters of fractional order chaotic system.

Keywords

• Parameter identification
• Fractional order chaotic system
• Imperialist competition algorithm
• Opposition based learning